Project Summary We propose to introduce and optimize a new method of radiomics extraction via transfer learning with deep convolutional neural networks (CNNs) and to compare it to the conventional segmentation-based radiomics approach on breast dynamic contrast-enhanced magnetic resonance images (DCE-MRIs). The field of breast radiomics has been expanding fast, with many clinical conclusions being successfully derived from medical images using qualitative analysis. In the past couple of years, deep learning has experienced explosive growth in image recognition, easily solving complex problems. Deep CNNs achieve remarkable classification results on everyday image datasets. We propose to investigate the utility of deep neural networks with regards to the medical image datasets, specifically on the breast DCE-MRI dataset. Given the relatively small sizes of these datasets, CNNs previously trained on non-medical images will be utilized for clinical classifications as feature extractors. We will investigate multiple parameters involved in the CNN feature extraction methodology and their effect on classification performance. Two clinical tasks will be studied under the proposed research: 1) malignancy assessment and 2) response to therapy prediction. The optimized CNN method will be compared to and combined with the conventional segmentation- based radiomics method. Furthermore, we aim to investigate the robustness of the segmentation-based features across MR scanners of different manufacturers. The first aim of the proposed research will study the robustness of the segmentation- based features extracted from images acquired on MR scanners of two different manufacturers. The robustness will be investigated under four clinical tasks, such as lymph node involvement and receptor statuses. The second aim will be focused on optimization of CNN feature extraction and subsequent classifier design. Lastly, under the third aim we will compare and combine the CNN and segmentation-based radiomics in the classification tasks of malignancy assessment and response to therapy prediction.